1. Design Seminar
on
HUMAN FACE IDENTIFICATION
Submitted for partial fulfillment of the degree of
Bachelor of Engineering
BY
1.Vishal Dhote 2.Bhupesh Lahare
3.Akash Bonde 4.Shrinath Wadyalkar
5.Nidhi Meshram
7th Semester
Department of Information Technology
Er.C.D.Bawankar Er. Ashvini Kheole Prof. S. V. Sonekar
Project Guide Project Incharge HOD(CSE/IT)
Department of Information Technology,
J D College of Engineering & Management, Nagpur.
Rashtrasant Tukdoji Maharaj Nagpur University, Nagpur.
Session: 2012-2013

2. Contents:
 Aim
 Objective
 Literature Survey
-Problem Definition
 Research Methodology
 Software Requirements
 Hardware Requirements
 Limitations
 Result
 Conclusion
 Bibliography

3. Aim:
 Face recognize that works under varying poses.
 Importance of faces
 Central role in human interactions
 Communicate a wealth of social information:
 Age, gender, personal identity (physical structure)
 Mood and emotional state (facial expression)

4. Objective
 Develope a computational model.
 Why face recognition?
To apply it to wide area of problems.
1)Criminal Identification
2)Security
3)Image and Film Processing

5. Literature Survey:
1. Avinash Kaushal1, J P S Raina, A., “Face Detection using
Eigenface method ,Gabor Wavelet Transform”, IJCST Vol. 1,
Iss ue 1, September 2010 I S S N : 0 9 7 6 - 8 4 9 1
Eigenface method, template matching, graph matching,
method. The eigenface approach applies the Karhonen-Loeve
transform for feature extraction. It greatly reduces the facial
feature dimension and yet maintains reasonable discriminating
power.
2. Steve Lawrence , Lee Giles “Face Recognition: A
Convolutional Eigenface method “ IEEE Transactions on
special issue on Pattern Recognition. vol.3, no110, 2009
Eigenface method, though some variants of the algorithm work
on feature extraction as well, mainly provides sophisticated
modeling scheme for estimating likelihood densities in the
pattern recognition phase.

6. Problem Definition :
 To retrieve the similar images(based on a heuristic)
from the given database of face images.
 It used to take much time to find any criminals
 Not very much accurate.
 Danger of losing the files in some case.

7. Research Methodology:
 Eigen face method is based on an information theory
approach that decomposes face images into a small set
of characteristic feature images called eigenfaces.
• Recognition is performed by projecting a new image
into the subspace[3].

8. Process Flow Diagram:
Start
Login
Authentication
Valid User
Invalid User
Main Screen
Add Image Clip Image Update Details Construct Image Search Process
Enter Make Clips Open Record Specify Feature
Search Image &
Details & Update
Get Details
Add to Add Clips to Add to Search
Result
Database Database database Image
End

9. Context Flow Diagram:
EYE WITNESS
FACE
OPERATOR IDENTIFICATION CRIMINAL
SYSTEM FACE

10. Login Process:
PROCESS
LOGIN SCREEN
ERROR IN
INPUT
LEVEL-0

11. Main Screen Process:
MAIN
OPERATOR SCREEN ADD IMAGE
SEARCH
IMAGE
CLIP IMAGE
CONSTRUCT
IMAGE
LEVEL-1

12. Add Image Process:
DATABASE
ADD
OPERATOR PROCESS DATA IS
ADDED
ERROR
LEVEL-2

13. Construct Image:
DATABASE HAIR
FOREHEAD
INSTRUCTION EYES
FACE
NOSE
LIPS
LEVEL-3

14. Clipping Process:
DATABASE DATABASE
EYES
NOSE
FACE FACE
HAIR
FOREHEAD
LEVEL-4

15. Clipping Process:

16. Update Process:
DATABASE
UPDATE DATA
OPERATOR
PROCESS UPDATED
LEVEL-5

17. Screenshot
Face Identification Main Screen: LOGIN

18. Screenshot
Face Identification Main Screen: File

19. Screenshot
Face Identification Main Screen: File

20. Screenshot
Face Identification Main Screen: File

21. Screenshot
Face Identification Main Screen: File

22. Screenshot
Face Identification Main Screen: File

23. Screenshot
Face Identification Main Screen: EDIT

24. Screenshot
Face Identification Main Screen: EDIT

25. Screenshot
Face Identification Main Screen: IDENTIFICATION

26. Screenshot
Face Identification Main Screen: IDENTIFICATION

27. Screenshot
Face Identification Main Screen: IDENTIFICATION

28. Screenshot
Face Identification Main Screen: HELP

29. Software Requirements:
 Language : VB.Net
 Operating System : Windows
 Database : SQL Server 2005

30. Hardware Requirements:
 Processor : Processor with 400 Mhz.
 Hard disk : 1 GB hard disk.
 RAM : 256MB
 Mouse : MS mouse or compatible.
 Keyboard : standard 101 or 102 Keys.

31. Limitations:
 Face Recognition Is Not Perfect And Struggles To
Perform Under Certain Conditions.
1. Poor Lighting
2.Other Objects Partially Covering The Subject’s
Face.
3.Low Resolution Images.
4.It is not platform independent

32. Result:
 Thus we have reduced the problem of matching faces
with previous applications.
 This application will find the approximate match of
human face at various angles.

33. Conclusion:
 A face recognition system must be able to recognize a
face in many different imaging situations.
 It will find faces efficiently without exhaustively
searching the image.
 Face recognition systems are going to have
widespread application in smart environments.
.

34. Bibliography:
[1] Avinash Kaushal1, J P S Raina, A., “Face Detection using Neural
Network & Gabor Wavelet Transform”, IJCST Vol. 1, Iss ue 1,
September 2010 I S S N : 0 9 7 6 - 8 4 9 1
[2]Steve Lawrence , Lee Giles “Face Recognition: A Convolutional
Neural Network Approach “ IEEE Transactions on Neural Networks,
Special Issue on Neural Networks and Pattern Recognition. vol.3,
no110, 2009
[3] Parvinder S. Sandhu, Iqbaldeep Kaur, “Face Recognition Using
Eigen face Coefficients and Principal Component Analysis”,
International Journal of Electrical and Electronics Engineering 3:8
2009 ISSN 0978-9481
[4] Stan Z. Li and Juwei Lu., “Face Recognition Using the Nearest
Feature Line Method” , IEEE TRANSACTIONS ON NEURAL
NETWORKS, VOL. 10, NO. 2, MARCH 1999 pp-439-443
[5] S. T. Gandhe, K. T. Talele, and A.G.Keskar “Face Recognition
Using Contour Matching” IAENG International Journal of Computer
Science, 35:2, IJCS_35_2_06

35. Thank You